Excited to share “Design-Based Inference for Spatial Experiments with Interference”, joint with Peter M. Aronow and
Ye Wang: arxiv
In settings with complex spatial effects and interference, the paper defines a type of marginal effect, the “average marginalized response,” that has a clear interpretation and can be identified with a spatial experiment and a simple contrast.
It took time to work out details for robust inference, and finally got there with Ye working out reasonable conditions that justify the spatial HAC variance estimator, and then by connecting to a breakthrough CLT result from Ogburn et al. (2020; arxiv link).
We are working on the public release of the R package and also a more didactic paper that walks through applications. Stay tuned for those.
Below is a Twitter thread in which I offer a perspective from my experience through EGAP (egap.org) on how to make effective use of pre-analysis plans and also research designs. The basic idea is that your research design and pre-analysis plan should serve as the basis of a discussion in which you can refine your design and analysis and gain buy-in from skeptics. A research design or pre-analysis plan that is never discussed publicly before it is implemented is a huge missed opportunity.
The thread was in response to a paper by Duflo et al. (linked in the thread) who focus mostly on pre-analysis plans as ways to bind yourself, without giving much consideration to the idea of using them as the basis of having an ex ante conversation about the research.
The thread is here:
If you click on the tweet below, you will get a conversation on open source options (essentially Python, Julia, and R) for students interested in getting started with structural estimation:
Among other things, people pointed to the following resources to get you started:
With Stephanie Zonszein, Dean Eckles, and Peter Aronow, we have a new review article on estimating spillover effects with experimental data, with accompanying R package:
At seminars one often hears “what about SUTVA violations?” Don’t just wave your hands, rather:
Learn what’s identified even w/ SUTVA violations of unspecified form–e.g., https://arxiv.org/abs/1711.06399.
Estimate the spillover effects–that’s what this review piece and accompanying R package are about.
As per Athey et al. (2019, Annals of Statistics), random forests can be interpreted as kernel estimators. If you haven’t seen this before, here is a toy example: .html